You're reading from Mastering Machine Learning Algorithms Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838820299

Length 798 pages

Edition 2nd Edition

Languages

Python

Tools

Keras

Concepts

Machine Learning

Authors (2):

Giuseppe Bonaccorso

View More author details

Table of Contents (28) Chapters

Preface

1. Machine Learning Model Fundamentals

2. Loss Functions and Regularization FREE CHAPTER

3. Introduction to Semi-Supervised Learning

4. Advanced Semi-Supervised Classification

5. Graph-Based Semi-Supervised Learning

6. Clustering and Unsupervised Models

7. Advanced Clustering and Unsupervised Models

8. Clustering and Unsupervised Models for Marketing

9. Generalized Linear Models and Regression

10. Introduction to Time-Series Analysis

11. Bayesian Networks and Hidden Markov Models

12. The EM Algorithm

13. Component Analysis and Dimensionality Reduction

14. Hebbian Learning

15. Fundamentals of Ensemble Learning

16. Advanced Boosting Algorithms

17. Modeling Neural Networks

18. Optimizing Neural Networks

19. Deep Convolutional Networks

20. Recurrent Neural Networks

21. Autoencoders

22. Introduction to Generative Adversarial Networks

23. Deep Belief Networks

24. Introduction to Reinforcement Learning

25. Advanced Policy Estimation Algorithms

26. Other Books You May Enjoy

27. Index

Introduction to Markov random fields

Let's consider a set of random variables, (normally drawn from the same distribution family despite there being no restrictions about the distributions that demand this must be so), organized in an undirected graph, G = {V, E}, as shown in the following diagram:

Example of a probabilistic undirected graph

Before analyzing the properties of the graph, we need to remember that two random variables, a and b, are conditionally independent given the random variable, c, if:

p(a, b|c) = p(a|c)p(b|c)

If all generic couples of subsets of variables are conditionally independent given a separating subset S_k (so that all connections between variables belonging to S_i to variables belonging to S_j pass through S_k), the graph is called a Markov random field (MRF).

Given G = {V, E}, a subset containing vertices such that every couple is adjacent is called a clique (the set of all cliques is often denoted as cl(G)). For example, consider...